Double replacement cation neutralization of high alkalinity waste materials

ABSTRACT

A method of treating alkaline industrial by-products, such as red mud generated by Bayer process bauxite refining, is described. Embodiments of the method comprise treating the alkaline industrial by-products with salts of divalent and/or polyvalent cations, thereby lowering pH of the alkaline industrial by-products. The method involves replacement reactions in which relatively insoluble hydroxide salts form precipitates, thereby removing hydroxide ions from solution.

FIELD OF THE INVENTION

This invention is involved with the treatment of waste and byproductmaterials to render them more suitable for disposal or reuse bypartially or significantly reducing the material's pH. Specifically,this invention relates to the treatment of moderate to highly hydroxidealkalinity bearing wastes and by-products.

BACKGROUND

There appears to be no patented art in the field of waste alkalinityreduction using inorganic salts; most if not all current technologyregarding high alkalinity reduction is done through the long standingmethod of using mineral acid. The invention shown below is the onlycurrent technology found in our patent search, but it does not utilizeany of the technology contained in this new application.

Abstract of U.S. Pat. No. 4,913,835

This invention relates to novel compositions and methods forneutralization and solidification of hazardous alkali spills. A dryparticulate composition containing an organic neutralizing acid and,materials having varying adsorption rates may be used to neutralizealkaline spills, and solidify the spills to render them harmless. Thesecompositions may be applied to the spills by fire extinguisher-likedelivery devices which spread the compositions on the spills from arelatively safe distance without splattering the hazardous materials.

Description of Preferred Embodiments of U.S. Pat. No. 4,913,835

The compositions of this invention preferably contain between about 45%and about 80% by weight of organic neutralizing acid in a dryparticulate form such as citric acid, fumaric acid, tartaric acid orbenzoic acid, between about 5% to about 45% by weight of a highlyabsorptive clay such as attapulgite, perlite, fullers earth orMinugel.RTM and the like, optionally between about 10% to about 45% byweight of less absorptive clay, such as attapulgus clay and the like andbetween about 0.5% and about 10% by weight of a water soluble weak acidsalt such as sodium dihydrogen phosphate, magnesium stearate, tricalciumphosphate, aluminum octoate, sodium stearate, monosodium salt ofdimethyl naphthalene sulfonate, sodium polyacrylate, and the like.

Abstract of U.S. Pat. No. 5,246,596

Disclosed is a method for processing waste to render it fit for ultimatedisposal. The method comprises first contacting together in a reactionmixture the waste stream, an ammonia source capable of evolving ammoniafor treating the waste, Ca(OH)₂, pozzolanic chemicals SiO₂, Fe₂O₃, andAl₂O₃, and at least one pozzolanic accelerator selected from the groupconsisting of anionic metal silicates, anionic carbon compounds, anionicboron compounds (borate family), anionic phosphorous compounds(phosphate family), and gelling enhancers, in a manner suitable to causepozzolanic stabilization reactions to occur thereby increasing thesolids percentage of the reaction mixture. Next, the reaction mixture isallowed to reach a target temperature of at least about 90° C., to reacha pH of at least about 11.5, and to evolve ammonia gas, thereby forminga heated mixture. Finally, the exposed surface area to mass ratio of thesolids in the heated mixture is increased in a manner suitable torelease the evolved ammonia gas, thereby neutralizing a substantialportion of the pathogens present in the waste stream and forming atreated waste stream that is suitable for ultimate disposal.

DETAILED DESCRIPTION

A method of treating hydroxide alkaline waste and by-products to renderthe material neutralized or reduced in pH from near 14 to a minimumlower limit of about 5.3 based on aluminum poly cation salts, renderingthe waste or by-product stream suitable for ultimate disposal or reuse.The method comprises contacting together a mixture of moderate to highalkaline waste or by-product material with a pH in the range of 7.5-14,with sufficient water added or found within the alkaline material, withone or more poly cationic salts in the dry form taken from a group ofsalts containing trivalent aluminum, trivalent iron, divalent calcium,divalent magnesium, divalent manganese, divalent zinc, or any polyvalentcationic salt that is soluble in water to a minimum extent of 0.5 gramsper 100 milliliters of water at near 0° C. and 2 grams per 100milliliters of water at near 100° C., in such a way to cause thesematerials to interact in a double replacement reaction to form a solublesalt reaction by-product and an insoluble hydroxide precipitate.

The added salts may be dry or made up of a brine or dilute salt solutionof the chosen salt or salts from the set of poly cation salts suitableto reduce hydroxide alkalinity in waste and by-product materials causingthe pH to drop immediately upon thorough mixing of the poly cation saltsand the waste/product materials, resulting in a treated waste/by-productstream/that is suitable for ultimate disposal or reuse.

Neutralization of Alkaline Wastes or by-Products

This invention is about the neutralization of high through low levelalkalinity with pH values in the range of 14-7.5 found in waste andby-product liquids, slurries, sludges and high solids systems that arecapable of being water diluted. This neutralization is completed throughthe use of low cost, low to non toxic, environmentally friendlypolyvalent cation salts. The resulting treated pH values can be anywherefrom about 13.9 to approximately 5.3. These salts may be introduced inthe dry form or in a variety of brine concentrations for maximumtreatment efficiency. The salt addition may be of one active salt or itmay be of a blend of salts some of which may be inactive single valentsalts like potassium and sodium chloride. The active ingredient saltsare based on polyvalent cations that are soluble in water to at least anextent of 0.5 gram in 100 grams of water at 0° C. or 1 gram per 100milliliters (mL) of distilled water at 100° C. Preferable solubilitieswould be in excess of 15 grams per 100 mL of distilled water at 20° C.The reactions of these salts are first order and will happen at oncewhen fully mixed into the waste or by-product. When the salt formula isadded dry, the reaction is seen in less than 5 minutes or when completemixing and dissolution occurs. Heavier and thicker solid slurries willcause a brief delay in full mixing and dissolution but in all studiedcases, the reduction of the pH of the material to approximately itsfinal pH is typically less than 15 minutes.

The examples of suitable polyvalent salts, based on the typicalcommercial quantity cost of each salt, its ready availability andoverall effectiveness are: (1) ammonium aluminum alum, (2) aluminumsulfate anhydrous, (3) aluminum sulfate.18H₂O, (4) magnesium chlorideanhydrous, (5) magnesium sulfate anhydrous, (6) magnesium sulfate.7H₂O,(7) calcium chloride anhydrous, (8) manganese chloride, (9) manganesechloride.4H₂O, (10) Sodium iron alum, (11) Zinc sulfate, (12) Zincsulfate.7H₂O.

It should be noted that any polyvalent cation salt that is sufficientlysoluble in water in the range of near freezing to near boiling, and willcarry out a double replacement reaction may be used in this invention toreduce hydroxide alkalinity in water, slurry and sludges with sufficientwater to dissolve the salt following adequate mixing. The addition ofthese salts causes water to separate from slurries and sludges.

Three balanced chemical equation examples of the caustic alkalinityremoval chemistry by the double replacement reaction are found in thefollowing formulas [I]:

Solubility in water for the hydroxide salts shown in [I] are: 0.185 gper 100 mL H2O at 212° F. for Ca(OH)₂; <0.009 g per 100 mL H2O at 68° F.for Al(OH)₃; and 0.004 g per 100 mL H2O at 212° F. and 0.0009 g at 68°F. for Mg(OH)₂.

A method of determining the effectiveness of each polyvalent cation inreducing different alkalinity concentrations is to use a set offactorial experiments. These were established and carried out. Astatistical factorial experiment is one conducted to investigate theeffect of two or more variable (factors) on the mean value of a responsevariable. Some of the key factorial and regression effectiveness studiesperformed in developing this patent application follow.

A four factor statistical analysis that involved the study of Calcium(Ca⁺²), Magnesium (Mg⁺²), Aluminum (Al⁺³), and Alkalinity Index (Aidx)was carried out using about 1.05 specific gravity alumina red mudbyproduct of 13 pH as well as an about 15% slurry of clay contaminatedwith fine iron filings and sodium and potassium hydroxide. The pH wasalso 13. The response variable was change in pH at 30, 60, 90 minutesand 24 hours. All salt was added in the dry form. An example of aFactorial Statistical Design used in the development of this technologyis shown in Table 1.

TABLE 1 0.296 0.54 0.119 31 0.16 8.5 0.197 0.36 0.238 37.5 0.071 8.30.099 0.18 0.12 44 0.0178 9.9 0.296 0.18 0.12 44 0.0533 8.55 0.099 0.540.12 44 0.054 9.5 0.099 0.54 0.357 31 0.054 6 0.197 0.36 0.238 37.50.071 8.2 0.099 0.18 0.357 44 0.0178 8.2 0.296 0.18 0.357 31 0.053 5.80.296 0.54 0.357 44 0.16 7.5 0.246 0.001 0.179 44 0.001 8.8 0.246 0.180.179 44 0.0443 8.7 0.296 0.36 0.001 44 0.001 9.6 0.247 0.36 0.119 37.50.0889 8.6 0.296 0.36 0.119 44 0.107 8.55 0.296 0.18 0.119 44 0.053 8.60.296 0.18 0.36 44 0.053 7.5 0.296 0.54 0.36 44 0.16 7.7 0.099 0.54 0.3631 0.053 6 0.099 0.54 0.12 31 0.053 8.1 0.099 0.54 0.119 44 0.053 9.50.099 0.54 0.119 44 0.053 9.6 Mg + 2 Ca + 2 Al + 3 Aidx Mg * Ca pH (30min) X1 X2 X3 X4 X1 * X2 Y

The resultant regression equation for the pH response is at 30 minutesof mixing: 6.74−5.27(Mg⁺²)−1.527(Ca⁺²)−7.55(Al⁺³)+0.104(Aidx)+8.28(Ca⁺²*Mg⁺²). The equation Coefficient of Determination (R²)=0.91;F-Ratio of 41.3 for model at a >99% confidence level. Multipleregression statistics are shown in Table 2.

TABLE 2 Dependent pH (30 min) response = Y, element analysis in gramsRegression Equation Section Independent Regression Standard T-Value ProbDecision Power Variable Coefficient Error (Ho: B = 0) Level (5%) (5%)Intercept 6.741584 0.8652696 7.7913 0.000001 Reject Ho 1.0000 Mg−5.272284 1.450637 −3.6345 0.002231 Reject Ho 0.9263 Ca −1.5223030.8352348 −1.8226 0.087105 Accept Ho 0.4027 Al −7.550253 0.7624828−9.9022 0.000000 Reject Ho 1.0000 Alkalinity Idx 0.1041732 1.552946E−026.7081 0.000005 Reject Ho 0.9999 Mg * Ca 8.27896 3.304713 2.50520.023428 Reject Ho 0.6528 R-Squared 0.928028 Regression CoefficientSection Independent Regression Standard Lower Upper StandardizedVariable Coefficient Error 95% C.L. 95% C.L. Coefficient Intercept6.741584 0.8652696 4.907294 8.575873 0.0000 Mg −5.272284 1.450637−8.347496 −2.197071 −0.4096 Ca −1.522303 0.8352348 −3.292922 0.2483156−0.2312 Al −7.550253 0.7624828 −9.166644 −5.933862 −0.7580 AlkalinityIdx 0.1041732 1.552946E−02 7.125223E−02 0.1370942 0.5002 Mg * Ca 8.278963.304713 1.273282 15.28464 0.3276 T-Critical 2.119905 Analysis ofVariance Section Sum of Mean Prob Power Source DF Squares Square F-RatioLevel (5%) Intercept  1 1500.677 1500.677 Model  5 26.04807 5.20961441.2620 0.000000 0.994345 Error 16 2.020112 0.126257 Total (Adjusted) 2128.06818 1.33658 Root Mean Square Error 0.3553266 R-Squared 0.9280 Meanof Dependent 8.259091 Adj R-Squared 0.9055 Coefficient of Variation4.302248E−02 Press Value 5.700381 Sum|Press Residuals| 8.140982 PressR-Squared 0.7969

This model is shown in Table 3, except it is expressed in pounds of theactual chemical molecules used per ton of waste treated.

TABLE 3 48 24 16 31 1152 8.5 32 16 32 37.5 512 8.3 16 8 16 44 128 9.9 488 16 44 384 8.55 16 24 16 44 384 9.5 16 24 48 31 384 6 16 16 32 37.5 2568.2 16 8 48 44 128 8.2 48 8 48 31 384 5.8 48 24 48 44 1152 7.5 40 0.00124 44 0.001 8.8 40 8 24 44 320 8.7 48 16 0.001 44 0.001 9.6 40 16 1637.5 640 8.6 48 8 16 44 384 8.55 48 8 16 44 384 8.6 48 24 48 44 1152 7.548 24 48 44 1152 7.7 16 24 48 31 384 6 16 24 16 31 384 8.1 16 24 16 44384 9.5 16 24 16 44 384 9.6 Mg + 2 Ca + 2 Al + 3 Aidx Mg * Ca pH (30min) X1 X2 X3 X4 X1 * X2 Y

This regression equation (in pounds) for the pH of the treated waste at30 minutes is shown in Table 4.

TABLE 4 Dependent pH (30 min) = Y Pounds of MgSO4*7H20, CaCl2_anhy,Ammonium Alum per ton waste Regression Equation Section IndependentRegression Standard T-Value Prob Decision Power Variable CoefficientError (Ho: B = 0) Level (5%) (5%) Intercept 6.902774 0.74432    9.27390.000000 Reject Ho 1.000000 Mg −3.603484E−02 7.921394E−03  −4.54910.000329 Reject Ho 0.989337 Ca −3.648528E−02 1.645241E−02  −2.21760.041405 Reject Ho 0.549233 Al −6.017634E−02 5.367156E−03 −11.21200.000000 Reject Ho 1.000000 Alkalinity Idx 0.1035826 1.349576E−02   7.6752 0.000001 Reject Ho 1.000000 Mg * Ca 1.368777E−03 4.189366E−04   3.2673 0.004842 Reject Ho 0.865627 R-Squared 0.944003 RegressionCoefficient Section Independent Regression Standard Lower UpperStandardized Variable Coefficient Error 95% C.L. 95% C.L. CoefficientIntercept 6.902774 0.74432 5.324886 8.480661 0.0000 Mg −3.603484E−027.921394E−03 −5.282744E−02 −1.924223E−02 −0.4699 Ca −3.648528E−021.645241E−02 7.136285E−02 −1.607724E−03 −0.2522 Al −6.017634E−025.367156E−03 −0.0715542 −4.879848E−02 −0.8100 Alkalinity Idx 0.10358261.349576E−02 7.497291E−02 0.1321924 0.4974 Mg * Ca 1.368777E−034.189366E−04 4.806712E−04 2.256883E−03 0.4252 T-Critical 2.119905Analysis of Variance Section Sum of Mean Prob Power Source DF SquaresSquare F-Ratio Level (5%) Intercept  1 1500.677 1500.677 Model  526.49646 5.299292 53.9463 0.000000 0.999425 Error 16 1.5717239.823267E−02 Total (Adjusted) 21 28.06818 1.33658 Root Mean Square Error0.3134209 R-Squared 0.9440 Mean of Dependent 8.259091 Adj R-Squared0.9265 Coefficient of Variation 0.0379486 Press Value 5.720131 Sum|PressResiduals| 7.59758 Press R-Squared 0.7962

The cation values shown in the test model in Table 1 are the total gramsof each target active metal in each experiment. To convert the metalgrams into the grams of actual salts used you must do the followingmathematical conversions:

Mg⁺² came from magnesium sulfate.7H₂O. Mg⁺² is 9.85% by weight of thismolecule.

Divide 0.30/0.0985=3 grams. Ca⁺² came from Calcium Chloride. Ca⁺² is 36%by weight of this molecule. Divide 0.54/0.36=1.5 grams. Al⁺³ came fromammonium aluminum sulfate.18H₂O. Al⁺³ is 11.9% by weight of thismolecule. Divide 0.36/0.119=3.0 grams.

Each value is converted in this manner. Note that each experiment wasbased on 125 grams of raw Aidx 31 to 44 waste/by-products. Use this as abasis to calculate how many pounds of salt per ton of waste would berequired.

The waste/by-product Aidx factor (Alkalinity Index factor value) shownin the study designs referenced above was determined as follows. Becausehigh alkalinity/high pH wastes can contain complex buffering solids suchas in the case of the alumina red mud, a direct acid neutralizationmethod was chosen to determine an alkalinity index that could be used inthe factorial design test scheme. Both waste materials in this studywere titrated with a 1 normal (1 N) solution of hydrochloric acid fromthe raw pH of about.13 to 6.0 pH. A curve and an equation describingeach of the two curves were generated. Next, each curve was integratedfrom pH 6 to 13. The resulting y/dx area value was used as the Aidx. Inthe case of red mud this integration value was 44, and the loweralkalinity waste, which was less complex, had an Aidx integration valueof 31. These Aidx values allowed the generation of a regression responseequation that included not only the impact of each polyvalent cation invarious salts but how these impacts would change as the strength of analkaline waste varied. Graphs of these waste neutralization curves withtheir representative regression equations and integral areas under eachcurve between pHs 6-13 are included here. Area under the curve isintegrated to yield the Aidx value to use in treatment regressionequations for individual treatment formulae and base normalities ofvarious red muds (spent bauxites).

A graph of red mud neutralization is shown in FIG. 1. Regressionequation and integral areas under the curve between pH 6-13 for FIG. 1are:

-   -   Y=mL 1N HCl/g of waste;    -   X=pH of red mud caustic slurry    -   Y=89.8−26.06X+2.96X²−0.1087X³    -   Standard Error=0.43 pH units    -   regression coefficient (r)=0.998    -   Integral of range 6 pH to 13=l(ydx)=44 area

A graph of lower alkalinity waste neutralization is shown in FIG. 2.Regression equation and integral areas under the curve between pH 6-13for FIG. 2 are:

-   -   Y=mL 1N HCL/g of waste;    -   X=pH of Waste #2 caustic slurry    -   Y=(12.25−0.95X)/(1+0.0798X−0.0118X²)    -   Standard Error=0.66 pH units    -   regression coefficient (r)=0.987    -   Integral of pH range 6−13=l(ydx)=31 area.

A three factor statistical analysis was run that involved the study ofCalcium (Ca⁺²), Magnesium (Mg⁺²) and temperature (° F.) of thewaste/salt mix. The response variable was change in pH at 15, 30, 60, 90minutes and 24 hours. All salt was added in the dry form and each testsample comprised 125 grams of raw Aidx 44 waste/by-product. This studydesign is shown in Table 5. Table 5 lists MgSO₄.7H₂O (“A Epsom gms” inthe table) and anhydrous CaCl₂ (“CaCl₂ _(—) Anh gms” in the table) ingrams of the salt, not quantity of cation.

TABLE 5 A Epsom CaCl2_Anh pH15 30-250 g pH45 pH60 gms gms C. Temp F. susmple su su su Std Dsn Id Run Block Factor Factor Factor ResponseResponse Response Response 7 7 1 1 1.50 2.00 110.00 11.55 6 6 2 1 5.500.80 110.00 9.60 10 0 3 1 3.50 1.40 85.00 10.80 5 5 4 1 1.50 0.80 110.0012.68 8 8 5 1 5.50 2.00 60.00 9.50 9 0 6 1 3.50 1.40 85.00 10.70 4 4 7 15.50 2.00 60.00 9.50 2 2 8 1 5.50 0.80 60.00 9.60 1 1 9 1 1.50 0.8060.00 12.70 3 3 10 1 1.50 2.00 60.00 11.60

A regression equation describing the relationship of the independentvariables and the dependent variable waste pH at 30 minutes of mixing isfound in Table 6.

TABLE 6 Mg (Epsom Salt), CaCl2-Anhy, Temp F. 250 gram red mud sampletreated Dependent pH (30 min) = Y; 250 Gram Samples of Aidx 44 Red Mud;Treatment Equation based on Total Chemical Additions (not on elementweights) Regression Equation Section Independent Regression StandardT-Value Prob Decision Power Variable Coefficient Error (Ho: B = 0) Level(5%) (5%) Intercept 14.1841 0.5545943   25.5756 0.000000 Reject Ho1.000000 Mg_ES −0.6572538 5.173846E−02 −12.7034 0.000015 Reject Ho1.000000 CaCl2_anhy −0.5450127 0.1724615  −3.1602 0.019560 Reject Ho0.749860 Temp F. −3.721212E−03 4.393587E−03  −0.8470 0.429497 Accept Ho0.111025 R-Squared 0.967302 Regression Coefficient Section IndependentRegression Standard Lower Upper Standardized Variable Coefficient Error95% C.L. 95% C.L. Coefficient Intercept 14.1841 0.5545943 12.8270615.54115 0.0000 Mg_ES −0.6572538 5.173846E−02 −0.7838532 −0.5306544−0.9727 CaCl2_anhy −0.5450127 0.1724615 −0.9670108 −0.1230145 −0.2420Temp F. −3.721212E−03 4.393587E−03 −1.447193E−02 7.02951E−03 −0.0671T-Critical 2.446912 Analysis of Variance Section Sum of Mean Prob PowerSource DF Squares Square F-Ratio Level (5%) Intercept 1 1171.3731171.373 Model 3 14.13385 4.711282 59.1666 0.000076 0.997085 Error 60.4777644 0.0796274 Total (Adjusted) 9 14.61161 1.623512 Root MeanSquare Error 0.2821833 R-Squared 0.9673 Mean of Dependent 10.823 AdjR-Squared 0.9510 Coefficient of Variation 2.607256E−02 Press Value1.578493 Sum|Press Residuals| 3.573037 Press R-Squared 0.8920

For Table 6, the regression equation is: pH @ 30 min.treatment=14.18−0.657(Mg_ES)−0.545(CaCl₂)−0.0037(Mixture temp, ° F.);the R² value for the equation is 0.95 and the model F-ratio of 59.1at >99% confidence. The Mg_ES ingredient is magnesium sulfate.7H₂O, theCaCl₂ _(—) anhy is industrial grade anhydrous CaCl₂, and the Temp Frepresents the temperature in ° F. of the raw waste treated.

A four factor statistical analysis that involved the study of Calcium(Ca⁺²) in two forms [calcium chloride and calcium sulfate.2H₂O],Magnesium (Mg⁺²), and Sodium Chloride (Na⁺), the latter to determine ifit interfered with the neutralization reactions of the calcium andmagnesium. The calcium sulfate, which fails the solubility criteria, wasexamined to see if there was any positive or negative impact on pHreduction. The response variable was change in pH at 5, 10, 15, 20, and30 minutes plus 24 hours. All salt was added in the dry form. The testmodel is shown in Table 7.

TABLE 7 Red Mud Neutralization Tests (addition in grams) RM NaCl CaCl2Gypsum Epsom pH/5 pH/10 pH/15 pH/20 pH/30 pH/60 pH/24 h 256.88 .2 1 .2 212.1 12.1 12.1 12.1 12.14 12.18 11.2 256.88 .2 3 .8 2 11.13 10.9 10.9210.92 11.0 11.06 10.47 256.88 .8 1 .2 6 9.62 9.61 9.61 9.62 9.62 9.629.4 250 .2 3 .2 6 9.24 9.24 9.17 9.17 9.17 9.14 9.3 250 .8 1 .8 2 10.9310.9 10.87 10.87 10.86 10.86 10.92 250 .5 2 .5 4 10.04 10.04 10.05 10.0610.06 10.09 10.18 250 .8 3 .8 6 9.3 9.3 9.31 9.3 9.25 9.28 9.44 250 .2 1.8 6 9.23 9.21 9.22 9.2 9.19 9.18 9.29 250 .8 3 .2 2 10.57 10.43 10.4310.51 10.51 10.56 10.74 250 2 6 9.3 9.3 9.28 9.28 9.26 9.3 9.47 Notes:At random choose Test#1, Test#4, Test#8, Test#10 to re-test pH of the 5min, 15 min, and 30 min samples of each test I hour later, and found nosignificant change in pH from the from the first 5 minutes, which provesthe chemical reaction is immediate with good mixing. The pH of theuntreated Red Mud was a pH 12.55 and tests were conducted at roomtemperature. The temperature of the red mud was 65.9 F.. Salinity willbe checked on any free water and on settled red mud on all samples whencalibration solution for salinity meter arrives. Will also check pH.[Kaiser Red Mud with SG of ~1.025 compared to PNB shipped buckets at~1.05; all test samples were 250 grams of Red Mud plus chemicaladditions.f]

The regression equation that describes the mean pH value of allmeasurements from 5 through 60 minutes as impacted by the salts is shownin Table 8. The factorial design for this work used the total weight ofeach salt, and not the key element's (Ca⁺², Mg⁺², Na⁺¹) weight. Eachtest sample comprised 250 grams of an Aidx 44 waste/by-product.

TABLE 8 Analysis of Mean pH 60 mn total @ 5 minute Intervals - TreatedBauxite Red Mud SUM OF MEAN F SOURCE SQUARES DF SQUARE VALUE PROB > FMODEL 7.64345 5 1.52869 9.32 0.0997 CURVATURE 0.01027 1 0.01027 0.060.8258 RESIDUAL 0.32810 2 0.16405 COR TOTAL 7.98182 8 ROOT MSE 0.40503R-SQUARED 0.96 DEP MEAN 10.15556 ADJ R-SQUARED 0.86 C.V. % 3.98827Case(s) with leverage of 1.0000: PRESS statistic not defined.COEFFICIENT STANDARD t FOR H0 FACTOR ESTIMATE DF ERROR COEFFICIENT = 0PROB > |t| INTERCEPT 10.167500 1 0.143200 A −0.207500 1 0.143200 −1.450.2843 B −0.287500 1 0.143200 −2.01 0.1825 C −0.077500 1 0.143200 −0.540.6426 D −0.842500 1 0.143200 −5.88 0.0277 AD 0.337500 1 0.143200 2.360.1425 CENTER POINT −0.107500 1 0.429600 −0.25 0.8258 Final Equation inTerms of Coded Factors Mean pH 60 mn = 10.16750 − 0.20750 * A −0.28750 * B − 0.07750 * C − 0.84250 * D + 0.33750 * A * D Final Equationin Terms of Uncoded Factors Mean pH 60 mn = 14.02750 − 2.94167 * NaCl −0.28750 * CaCl2 − 0.25833 * Gypsum − 0.70250 * Epsom Salt + 0.56250 *NaCl * Epsom Salt OBS ACTUAL PREDICTED STUDENT COOK'S OUTLIER RUN ORDVALUE VALUE RESIDUAL LEVER RESID DIST T VALUE ORD 1 12.12 11.92 0.2000.750 0.988 0.418 0.976 6 2 9.62 9.82 −0.200 0.750 −0.988 0.418 −0.976 33 9.19 8.99 0.205 0.750 1.012 0.439 1.025 8 4 10.05 10.26 −0.205 0.750−1.012 0.439 −1.025 2 5 9.20 9.41 −0.205 0.750 −1.012 0.439 −1.025 9 610.88 10.68 0.205 0.750 1.012 0.439 1.025 5 7 10.99 11.19 −0.200 0.750−0.988 0.418 −0.976 7 8 9.29 9.09 0.200 0.750 0.988 0.418 0.976 4 910.06 10.06 −0.000 1.000 0.000 0.000 0.000 1

The mean pH values for each test from the model shown in Table 7 areshown in Table 9.

Also shown in Table 9 are the standard deviation in the pH values overthe 60 minute test period, as well as the difference between the mean 60minute pH value and the pH after 24 hours.

TABLE 9 12.12 0.033 −0.92 10.99 0.092 −0.52 9.62 0.0052 −0.22 9.19 0.0420.11 10.88 0.028 0.04 10.06 0.019 0.12 9.29 0.022 0.15 9.2 0.019 0.0910.5 0.061 0.2 9.9 0.016 0.18 X1 X2 X3 X1 = Mean value of all pHmeasurements in the NaCl, CaCl2, CaSO4*2H220 and MgSO4*7H20 studystarting with 5 minutes at 5 minute intervals to 60 minutes. X2 =Standard deviation of all mean values of X1 X3 = Difference in the pH at24 hours for each experiment and the 60 summary mean of X1 column.

This study was carried out to determine the impact of a single valentsalt, NaCl, on a mix of active salts (CaCl₂, MgSO₄.7H₂O) and todetermine if the essentially insoluble CaSO₄.2H₂O (gypsum) wouldpositively impact the reduction of alkalinity/pH in the treated waste.

The regression equation explaining the impact of these independentvariables on the standard deviation of the pH value of measurements at 5minute intervals from 5 minutes to 60 minutes is:

2.36+0.97*(NaCl)+1.76*(CaCl₂)+4.7*(CaSO₄.2H₂O)−0.73*(MgSO₄.H₂O)−8.5*(NaCl)*(CaSO₄.2H₂O); the R² value for the model is 0.95 with an F Ratio of26.24 at a confidence of 94%, as shown in Table 10.

TABLE 10 Analysis of Stnd Dev Summary of pH's at 5 minute intervals to60 mins. SUM OF MEAN F SOURCE SQUARES DF SQUARE VALUE PROB > F MODEL54.42300 5 10.8846 26.24 0.0371 CURVATURE 2.76909 1 2.7691 6.68 0.1228RESIDUAL 0.82960 2 0.4148 COR TOTAL 58.02169 8 ROOT MSE 0.64405R-SQUARED 0.98 DEP MEAN 3.46889 ADJ R-SQUARED 0.95 C.V. % 18.56645Case(s) with leverage of 1.0000: PRESS statistic not defined.COEFFICIENT STANDARD t FOR H0 FACTOR ESTIMATE DF ERROR COEFFICIENT = 0PROB > |t| INTERCEPT 3.665000 1 0.227706 A −0.985000 1 0.227706 −4.330.0495 B 1.760000 1 0.227706 7.73 0.0163 C 0.135000 1 0.227706 0.590.6134 D −1.460000 1 0.227706 −6.41 0.0235 AC −0.765000 1 0.227706 −3.360.0783 CENTER POINT −1.765000 1 0.683118 −2.58 0.1228 Final Equation inTerms of Coded Factors Stnd Dev 60 m = 3.66500 − 0.98500 * A + 1.76000 *B + 0.13500 * C − 1.46000 * D − 0.76500 * A * C Final Equation in Termsof Uncoded Factors Stnd Dev 60 m = 2.35667 + 0.96667 * NaCl + 1.76000 *CaCl2 + 4.70000 * Gypsum − 0.73000 * Epsom Salt − 8.50000 * NaCl *Gypsum OBS ACTUAL PREDICTED STUDENT COOK'S OUTLIER RUN ORD VALUE VALUERESIDUAL LEVER RESID DIST T VALUE ORD 1 3.30 3.45 −0.150 0.750 −0.4660.093 −0.349 6 2 0.52 0.09 0.430 0.750 1.335 0.764 2.867 3 3 4.20 4.050.150 0.750 0.466 0.093 0.349 8 4 6.10 6.53 −0.430 0.750 −1.335 0.764−2.867 2 5 1.90 2.33 −0.430 0.750 −1.335 0.764 −2.867 9 6 1.90 1.750.150 0.750 0.466 0.093 0.349 5 7 9.20 8.77 0.430 0.750 1.335 0.7642.867 7 8 2.20 2.35 −0.150 0.750 −0.466 0.093 −0.349 4 9 1.90 1.90 0.0001.000 0.000 0.000 0.000 1

The regression equation that describes the impact on the difference inmean pH for each test protocol over 60 minutes and at 24 hours is shownin Table 11.

TABLE 11 Analysis of pH 24 H-60u: Difference in Summary 60 min. mean pHand pH at 24 Hrs. Bauxite red mud test sample, 250 gram raw sample size.SUM OF MEAN F SOURCE SQUARES DF SQUARE VALUE PROB > F MODEL 1.12616 50.22523 54.11 0.0182 CURVATURE 0.05500 1 0.05500 13.21 0.0680 RESIDUAL0.00833 2 0.00416 COR TOTAL 1.18949 8 ROOT MSE 0.06452 R-SQUARED 0.99DEP MEAN −0.10111 ADJ R-SQUARED 0.97 C.V. % −63.80846 Case(s) withleverage of 1.0000: PRESS statistic not defined. COEFFICIENT STANDARD tFOR H0 FACTOR ESTIMATE DF ERROR COEFFICIENT = 0 PROB > |t| INTERCEPT−0.128750 1 0.022810 A 0.181250 1 0.022810 7.95 0.0155 B 0.123750 10.022810 5.43 0.0323 C 0.068750 1 0.022810 3.01 0.0947 D 0.161250 10.022810 7.07 0.0194 AD −0.248750 1 0.022810 −10.91 0.0083 CENTER POINT0.248750 1 0.068431 3.64 0.0680 Final Equation in Terms of Coded FactorspH 24 H-60u = −0.12875 + 0.18125 * A + 0.12375 * B + 0.06875 * C +0.16125 * D − 0.24875 * A * D Final Equation in Terms of Uncoded FactorspH 24 H-60u = −1.94458 + 2.26250 * NaCl + 0.12375 * CaCl2 + 0.22917 *Gypsum + 0.28792 * Epsom Salt − 0.41458 * NaCl * Epsom Salt OBS ACTUALPREDICTED STUDENT COOK'S OUTLIER RUN ORD VALUE VALUE RESIDUAL LEVERRESID DIST T VALUE ORD 1 −0.92 −0.91 −0.008 0.750 −0.232 0.023 −0.167 62 −0.22 −0.23 0.007 0.750 0.232 0.023 0.167 3 3 0.11 0.15 −0.045 0.750−1.395 0.834 −6.000 8 4 0.24 0.19 0.045 0.750 1.395 0.834 6.000 2 5 0.090.04 0.045 0.750 1.395 0.834 6.000 9 6 0.04 0.08 −0.045 0.750 −1.3950.834 −6.000 5 7 −0.52 −0.53 0.007 0.750 0.232 0.023 0.167 7 8 0.15 0.16−0.008 0.750 −0.232 0.023 −0.167 4 9 0.12 0.12 0.000 1.000 0.000 0.0000.000 1

The regression equation that describes the impact on salinitydifferences, measured in parts per million, on the treated sample decantwater after 24 hours of standing, is shown in Table 12. The untreatedtest sample material (about a 40 A idx red mud) had a free mud separatedwater salinity of 14,600 ppm.

TABLE 12 Analysis of Salinity-Decant Water @24 hours of room temperaturereaction time NaCl, CaCl2, CaSO4*2H20 and MgSO4*7H20, ppm SUM OF MEAN FSOURCE SQUARES DF SQUARE VALUE PROB > F MODEL 47186250.00 5 9437250.033.55 0.0292 CURVATURE 40138.89 1 40138.9 0.14 0.7419 RESIDUAL 562500.002 281250.0 COR TOTAL 47788888.89 8 ROOT MSE 530.33 R-SQUARED 0.99 DEPMEAN 14988.89 ADJ R-SQUARED 0.96 C.V. % 3.54 Case(s) with leverage of1.0000: PRESS statistic not defined. COEFFICIENT STANDARD t FOR H0FACTOR ESTIMATE DF ERROR COEFFICIENT = 0 PROB > |t| INTERCEPT 15012.50001 187.5000 A 1262.5000 1 187.5000 6.73 0.0214 B 1987.5000 1 187.500010.60 0.0088 C −162.5000 1 187.5000 −0.87 0.4775 D 462.5000 1 187.50002.47 0.1325 AD −337.5000 1 187.5000 −1.80 0.2137 CENTER POINT −212.50001 562.5000 −0.38 0.7419 Final Equation in Terms of Coded FactorsSalinity-Decant = 15012.50 + 1262.50 * A + 1987.50 * B − 162.50 * C +462.50 * D − 337.50 * A * D Final Equation in Terms of Uncoded FactorsSalinity-Decant = 7154.17 + 6458.33 * NaCl + 1987.50 * CaCl2 − 541.67 *Gypsum + 512.50 * Epsom Salt − 562.50 * NaCl * Epsom Salt OBS ACTUALPREDICTED STUDENT COOK'S OUTLIER RUN ORD VALUE VALUE RESIDUAL LEVERRESID DIST T VALUE ORD 1 1.15E+04 1.11E+04   3.75E+02 0.750 1.414 0.8570.000 6 2 1.42E+04 1.46E+04 −3.75E+02 0.750 −1.414 0.857 0.000 3 31.67E+04 1.67E+04   1.13E−13 0.750 0.000 0.000 0.000 8 4 1.83E+041.83E+04 −1.03E−15 0.750 −0.000 0.000 −0.000 2 5 1.24E+04 1.24E+04−3.42E−13 0.750 −0.000 0.000 −0.000 9 6 1.40E+04 1.40E+04 −1.03E−150.750 −0.000 0.000 −0.000 5 7 1.44E+04 1.48E+04 −3.75E+02 0.750 −1.4140.857 0.000 7 8 1.86E+04 1.82E+04   3.75E+02 0.750 1.414 0.857 0.000 4 91.48E+04 1.48E+04   2.07E−13 1.000 0.000 0.000 0.000 1

A two factor statistical analysis that involved the study of Mg⁺² andAl⁺³ with the response variable being the waste pH at 30 minutes. Anexample factorial test model for this pair of elements is shown in Table13.

TABLE 13 Al + 3 gms/ Mg + 2 gms/ pH (30 mn) 125 gms 125 gms su Std DsnId Run Block Factor Factor Response 6 3 1 1 0.001 0.394 10.100 3 2 2 10.476 0.001 7.500 7 4 3 1 0.476 0.394 7.050 5 3 4 1 0.001 0.394 10.000 42 5 1 0.476 0.001 7.400 9 0 6 1 0.238 0.195 8.900 2 1 7 1 0.001 0.00112.900 10 0 8 1 0.238 0.195 9.000 8 4 9 1 0.476 0.394 7.200 1 1 10 10.001 0.001 13.000

The regression equation and detail that describes these relationships isfound in Table 14. The weight of each element ion grams is used in thisstudy and each test sample was comprised of 125 grams of an Aidx 44waste/by-product.

TABLE 14 Analysis of pH @ 30 min. mixing - Al + 3 and Mg + 2 Study - 125gm. Aidx 44 □ Red Mud waste samples□ SUM OF MEAN F SOURCE SQUARES DFSQUARE VALUE PROB > F MODEL 43.99121 3 14.6637 243.69 <0.0001 RESIDUAL0.36104 6 0.0602 *LACK OF FIT 0.32979 1 0.3298 52.77 0.0008 *PURE ERROR0.03125 5 0.0062 COR TOTAL 44.35225 9 ROOT MSE 0.24530 R-SQUARED 0.99DEP MEAN 9.30500 ADJ R-SQUARED 0.99 C.V. % 2.63623 PRED R-SQUARED 0.98Predicted Residual Sum of Squares (PRESS) = 0.6665 *Residual =Lack-Of-Fit + Pure Error COEFFICIENT STANDARD t FOR H0 FACTOR ESTIMATEDF ERROR COEFFICIENT = 0 PROB > |t| INTERCEPT 9.302951 1 0.077571 A−2.106250 1 0.086727 −24.29 <0.0001 B −0.805095 1 0.086726 −9.28 <0.0001AB 0.643750 1 0.086727 7.42 0.0003 Final Equation in Terms of CodedFactors pH (30 mn) = 9.30295 − 2.10625 * A − 0.80509 * B + 0.64375 * A *B Final Equation in Terms of Uncoded Factors pH (30 mn) = 12.87701 −11.59274 * Al + 3 − 7.38705 * Mg + 2 + 13.79403 * Al + 3 * Mg + 2 OBSACTUAL PREDICTED STUDENT COOK'S OUTLIER RUN ORD VALUE VALUE RESIDUALLEVER RESID DIST T VALUE ORD 1 13.00 12.86 0.142 0.474 0.798 0.144 0.77110 2 12.90 12.86 0.042 0.474 0.236 0.013 0.216 7 3 7.50 7.36 0.142 0.4740.798 0.144 0.771 2 4 7.40 7.36 0.042 0.474 0.236 0.013 0.216 5 5 10.009.96 0.040 0.476 0.223 0.011 0.205 4 6 10.10 9.96 0.140 0.476 0.7860.140 0.758 1 7 7.05 7.04 0.015 0.476 0.082 0.002 0.075 3 8 7.20 7.040.165 0.476 0.927 0.195 0.914 9 9 8.90 9.31 −0.413 0.100 −1.776 0.088−2.353 6 10  9.00 9.31 −0.313 0.100 −1.346 0.050 −1.470 8

pH @ 30 minute mixing=12.88−11.59*(Al⁺³)−7.39*(Mg⁺²)+13.8(Al⁺³*Mg⁺²); R²for this equation is 0.99 with an F value for the model of 243 at >99%confidence.

A single variable study was carried out on the elements Al⁺³, Ca⁺², andMg⁺² individually to check their impact on the pH of raw test samplescomprising an Aidx 44 alumina by-product red mud 17-20% solids slurry.

The raw test sample had a pH of 13 and specific gravity of 1.055 priorto treatment. A graph of treatment of a 125 gram sample with Al⁺³ isshown in FIG. 3; a graph of treatment of a 125 gram sample with Ca⁺² isshown in FIG. 4, and a graph of treatment of a 125 gram sample with Mg⁺²is shown in FIG. 5. Relevant statistics and regression equations and forFIGS. 3, 4, and 5 are as follows.

FIG. 3: 3rd degree Polynomial Fit: y=a+bx+cx²+dx³;

-   -   Coefficient Data: a=10.8; b=3.48; c=0.390; d=−0.0146    -   Correlation Coefficient r=0.996; 0.99=R²;    -   Standard Error in Y=0.016 g Al⁺³

FIG. 4: Model: y=(a+bx)/(1+cx+dx²)

-   -   Coefficient Data: a=61.3; b=−4.32; c=−26.3; d=2.56    -   Equation limited at X=˜10.3    -   Correlation Coefficient: 0.985 R²=0.97;    -   Standard Error in Y=0.17 g Ca⁺²

FIG. 5: Model: y=a+bx+c/X²

-   -   Coefficient Data: a=−2.98; b=0.138; c=202    -   Correlation Coefficient: 0.997; R²=0.99;    -   Standard Error in Y=0.01 g Mg⁺2

Regression equations expressed in terms of the gram mass of the elementsAl⁺³, Ca⁺², Mg⁺² and their pH, which are derived from graphs shown inFIGS. 3, 4, and 5, are shown below:

Al⁺³:

pH of Al⁺³ treated 44 Aidx RedMud=10.82−3.48*(Al⁺³)+0.390*(Al⁺³)₂−0.0146*(Al⁺³)₃;

R² for this equation is 0.99 with a standard error of 0.016 Al⁺³.

Ca⁺²:

pH of Ca⁺² treated 44 Aidx RedMud=(61.3−4.32*(Ca⁺²))/(1-26.3*(Ca⁺²)+2.56*(Ca⁺²)²);

R² for this equation is 0.97 with a standard error of 0.17 g Ca⁺².

Mg⁺²:

pH of Mg⁺² treated 44 Aidx Red Mud=−2.98+0.138*(Mg⁺²)+202/(Mg⁺²)²

R² for this equation is 0.99 with a standard error of 0.01 g Mg⁺²

Purpose of the Neutralization of Waste or by-Products

Highly alkaline wastes and by-products, due to hydroxide concentrations,are often deemed dangerous or hazardous materials due to a high pH(typically >12) and must be rendered to a lower pH. It is desirous tominimize any volume or mass increase in this endeavor. Typicalneutralization approaches fail to minimize waste mass increases.

If an alkaline waste is at a pH that is not deemed hazardous by localagencies, a lower pH is often desirable so that the alkaline material ismore suitable for waste storage or for possible beneficial reuse.

An example of a large volume alkaline waste located at many placesaround the world from sites in East and West Europe, Africa, East andWest Asia, Latin America, and North America is a highly alkaline spentbauxite residual from the Bayer process utilized by the alumina/aluminumindustry. This alumina production process by-product is genericallycalled “red mud.” “Red mud” usually has a pH of over 13 when freshlyproduced due to the presence of residual sodium hydroxide. “Red mud”material is typically over pH 12 after aging in the drying lagoons orimpoundments found at the alumina plant sites around the world. Therehave been approximately 6-7 million metric tons of “red mud” generatedin the period 1990 through the second quarter 2003 (based on 1 ton redmud per ton of bauxite processed).

The use of this invention's poly cation salt neutralization technologywould make the “red mud” amenable for safer long term storage and renderit to a form that would make it suitable for study in the application ofmany possible recycle uses such a waste water filtration material, landamendments and restoration, and other waste treatments, all which wouldhave economic as well as environmental benefit. Further, thisinvention's poly cation neutralization treatment significantly minimizesan increase in mass and/or volume of the “red mud” by-product.

A second example of a lower worldwide volume, highly alkaline, due topotassium hydroxide, waste material is found in the “alkaline battery”industry. The neutralization of this waste material with this polycationic invention renders the alkaline battery waste non hazardous dueto alkalinity while minimizing waste volume and mass and rendering itsuitable for additional processing.

BEST MODE FOR CARRYING OUT THE INVENTION

This invention is suitable for application to any liquid, slurry, sludgeor solid that may be wetted with water to a sludge with suitable saltdissolving ability to cause the salt, double cation exchangeneutralization reaction to proceed. The salt or salt mixture chosen torun this neutralization reaction should be a salt with a solubility in100 grams of water at room temperature of at least 0.5 grams. Polycation salts such as CaSO₄.0.5H₂O or CaSO₄.2H₂O do not meet thisrequirement, yet each will have a small impact on reducing alkalinity ina waste or by-product, as shown in the invention description.

It is desirable to use a highly soluble salt such as ammonium aluminumalum or sodium iron (III) alum or magnesium sulfate heptahydrate ormagnesium chloride or calcium chloride. Such salts will (a) thoroughlydissolve in water at temperatures around water's freezing to boilingpoints to an extent exceeding 15 grams per 100 mL of water and (b)produce a double substitution reaction hydroxide product such as shownin equation [I] that is at least as insoluble in water as Ca(OH)₂ is incold and hot water.

The di-valent cation calcium is not the best element to use, if to beused alone, since it produces a slightly soluble precipitate in thedouble replacement reaction which causes the calcium cation to hit amaximum pH reduction limit near pH 10.5. A better mode would be to usethe aluminum tri-valent cation from one of its several soluble salts.

The best mode decision for reducing a waste or by-product's hydroxidealkalinity could be controlled by what sort of eventual end use for theneutralized product is envisioned at the time of treatment. An examplewould be the neutralization of spent bauxite-alumina “red mud” for theeventual purpose of blending it into a useable soil material. In thiscase, the straight use of the neutralization efficient cation aluminummight be modified to include some magnesium and/or calcium in theneutralization process in order to make the treated material's agronomiccharacteristics better. If long term storage was the goal, then the useof aluminum would maximize results and minimize chemical treatmentcosts.

A Method of Double Replacement Cation Neutralization of High AlkalinityWaste Materials

One embodiment of double replacement cation neutralization of highalkalinity waste materials comprises treating alkaline waste andby-products. The material is thus rendered, by use of aluminum polycation salts, neutralized or reduced in pH, from near 14 to a minimumlower limit of about 5.3. The resulting waste or by-product stream issuitable for disposal or reuse.

Treatment includes contacting together, in a reaction mixture the highpH waste or by-product material, with sufficient water added or foundwithin the alkaline material, with one or more poly cationic salts inthe dry form taken from a group of salts containing trivalent aluminum,trivalent iron, divalent calcium, divalent zinc, divalent magnesium,divalent manganese or any polyvalent cationic salt that is soluble inwater to a minimum extent of 0.5 grams per 100 milliliters of water atnear 0° C., and 2 grams per 100 milliliters of water at near 100° C., insuch a way to cause these materials to react to form a soluble saltreaction by-product and an insoluble hydroxide precipitate.

The level of pH reduction chosen and selection of the neutralizingcation or cations and the salt(s) in which they appear will control thepercentage of each and total salt added to the individualwaste/by-product reaction mixture.

Control of the chemical costs and the type and quantity of added metalcontent in the alkalinity/pH reduced material is facilitated.

The salt(s) may be dry or made up of a brine or dilute salt solution ofthe chosen salt. Salts are from the set of previously described polycation salts suitable to reduce hydroxide alkalinity in waste andby-product materials. Addition of the poly cation salts causes the pH todrop immediately upon thorough mixing of the poly cation salts and thewaste/product materials. The term “immediate,” as used in thisspecification and appended claims, refers to a range from instantly uponcomplete dissolution of the poly cation salt(s), to within less than 30minutes when the mixing process is difficult.

The temperature of the waste or by-product does not change the time ofreaction meaningfully and in turn the pH level of the treatedwaste/by-product within 15 minutes of treatment. Accordingly, a processtemperature of 60° C. versus a process temperature of 5° C. will showthat the difference in pH within the first 1 to 30 minutes will be lessthan 0.5 pH units lower at the higher temperature for most wastes; and,more commonly less than 0.35 pH units.

In some embodiments, the poly cation salt neutralization processrequires a determination of the level of alkalinity present in the wasteor by-product in order to determine how much active ingredient needs toadded in order to achieve the desired waste/by-product pH level.

This may be done through the use of the Alkalinity Index (Aidx) of thewaste or by-product, which is determined by titrating the “to betreated” material with a 1 Normal Hydrochloric Acid, measuring the pHfrom the raw pH to a pH of 6.0. The curve generated by this titrationshould be integrated to yield an “area under the curve” of millilitersof 1 Normal HCl per gram of alkaline waste per change in waste pH, asshown in graphs presented in FIGS. 1 and 2. This area value becomes theAidx value;

The Aidx value can then be used in a regression equation such as foundin Table 2, as well as in the generation of a new factorial test designsuch as the examples provided in Tables 1, 5, and 13, leading to aprocess specific regression equation which will allow the optimizationof the level of the specific poly cations chosen to treat the waste orby-product.

1. A method of treating an alkaline by-product comprising: combining a cation with the alkaline by-product, the alkaline by-product having a pH of 7.5-14, and the cation having a valence of +2 or greater and being in a physical form selected from the group consisting of (i) an undissolved salt and (ii) a salt dissolved in water at a concentration of 15 grams or more of the salt per 100 mL water; and generating a precipitate, the precipitate comprising the cation and a hydroxide ion.
 2. The method of claim 1, wherein the cation is selected from the group consisting of Al⁺³, Fe⁺³, Mg⁺², Zn⁺², and Mn⁺².
 3. The method of claim 2, wherein the alkaline by-product is red mud generated by a Bayer process.
 4. A method of creating a hydroxide salt comprising combining a neutralization salt with an alkaline by-product, the alkaline by-product having a pH of 7.5-14 and the neutralization salt being in at least one of two physical forms, the two physical forms being (i) an undissolved solid, and (ii) dissolved in water at a concentration of 15 grams or more of the neutralization salt per 100 mL water, wherein the hydroxide salt includes a cation and a hydroxide ion at a ratio of two or more of the hydroxide ions for each one of the cations.
 5. The method of claim 4, wherein the hydroxide salt is precipitated from aqueous solution.
 6. The method of claim 5, wherein the alkaline by-product is red mud generated by a Bayer process.
 7. The method of claim 6, wherein the cation is selected from the group consisting of Al⁺³, Fe⁺³, Mg⁺², Zn⁺², and Mn⁺². 